Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) procedure(s) can measure the transit of a tracer such as a gadolinium-chelate to estimate physiologic parameters such as perfusion or permeability in vivo. Applications of DCE-MRI procedure(s) can include estimates of tumor angiogenesis (see, e.g., Barrett T. et al., MRI of tumor angiogenesis, J Magn Reson Imaging, 26(2):235-249 (2007), and Kiessling F. et al., Contrast agents and applications to assess tumor angiogenesis in vivo by magnetic resonance imaging, Current medicinal chemistry, 14(1):77-91 (2007)), and response to therapy (see, e.g., Turnbull L. W., Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR in biomedicine (2008), and Marcus C. D. et al., Imaging techniques to evaluate the response to treatment in oncology: Current standards and perspectives. Critical reviews in oncology/hematology (2008), as well as physiologic measurements of organ function such as kidney glomerular filtration rates and perfusion (see, e.g., Lee V. S. et al., Renal Function Measurements from MR Renography and a Simplified Multicompartmental Model, Am J Physiol Renal Physiol, 292: F1548-1559 (2007); Zhang J. L., et al., Functional assessment of the kidney from magnetic resonance and computed tomography renography: impulse retention approach to a multicompartment model, Magn Reson Med; 59(2):278-288 (2008); Hackstein N., et al., Glomerular filtration rate measured using the Patlak plot technique and contrast-enhanced dynamic MRI with different amounts of gadolinium-DTPA, J Magn Reson Imaging; 22(3):406-414 (2005)). Performance can use a direct, well-controlled injection of the tracer bolus into the feeding vessel. Observed tissue concentration versus time curves can then reflect regional/local perfusion, permeability, or volume fraction, with minimal confounding effects due to the shape of the input function. However, for practical reasons, tracers can typically be injected intravenously, which can resulting in unpredictable dilution and/or widening of the bolus by the time it arrives at the feeding vessels, for example. Therefore, accurate quantitative analysis of DCE-MRI data can involve an individually measured arterial input function (AIF). Reliable measurement of AIF can be important to, e.g., the precision of determining the function of organ or tumor (see, e.g., Roberts C., et al., Comparison of errors associated with single- and multi-bolus injection protocols in low-temporal-resolution dynamic contrast-enhanced tracer kinetic analysis, Magn Reson Med; 56(3):611-619 (2006); Wang Y., et al., Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data, Magn Reson Med; 59(5):1183-1189 (2008); Peeters F., et al., Inflow correction of hepatic perfusion measurements using T1-weighted, fast gradient-echo, contrast-enhanced MRI, Magn Reson Med; 51(4):710-717 (2004)).
There can be several challenges to determine AIF. First, a relationship between MR signal intensity and a gadolinium concentration can be nonlinear, and can even be non-monotonic (see, e.g., Bokacheva L., et al., Quantitative determination of Gd-DTPA concentration in T(1)-weighted MR renography studies, Magn Reson Med; 57(6):1012-1018 (2007); Materne R., et al., Assessment of hepatic perfusion parameters with dynamic MRI, Magn Reson Med; 47(1):135-142 (2002)). Second, MR signal measurements from a blood vessel can be distorted by multiple artifacts, including, e.g., inflow effect (see, e.g., Peeters F., et al., Inflow correction of hepatic perfusion measurements using T1-weighted, fast gradient-echo, contrast-enhanced MRI, Magn Reson Med; 51(4):710-717 (2004); Ivancevic M. K., et al., Inflow effect correction in fast gradient-echo perfusion imaging, Magn Reson Med; 50(5):885-891 (2003)), dephasing (see, e.g., Heilmann M., et al., Simultaneous dynamic T1 and T2* measurement for AIF assessment combined with DCE MRI in a mouse tumor model, Magma (New York, N.Y.; 20(4):193-203 (2007), B1 inhomogeneity (see, e.g., Wang J., et al., Factors influencing flip angle mapping in MRI: RF pulse shape, slice-select gradients, off-resonance excitation, and B0 inhomogeneities, Magn Reson Med; 56(2):463-468 (2006); Warntjes J. B., et al., Novel method for rapid, simultaneous T1, T*2, and proton density quantification, Magn Reson Med; 57(3):528-537 (2007); Wang J., et al., T1 measurements incorporating flip angle calibration and correction in vivo, J Magn Reson; 182(2):283-292 (2006), Cheng H. L., et al., Rapid high-resolution T(1) mapping by variable flip angles: accurate and precise measurements in the presence of radiofrequency field inhomogeneity, Magn Reson Med; 55(3):566-574 (2006); van der Schaaf I., et al., Influence of partial volume on venous output and arterial input function, Ajnr; 27(1):46-50 (2006)), partial volume effect, (see, e.g., van der Schaaf I., et al., Influence of partial volume on venous output and arterial input function, Ajnr; 27(1):46-50 (2006); Chen J. J., et al., Partial volume effect in quantitative magnetic resonance perfusion imaging, Conf Proc IEEE Eng Med Biol Soc; 2:1132-1135 (2004)), and effects of flow pulsatility and turbulence.
Different approaches have been described to compute tracer concentration C(t). What can be considered to be a simple approach can be one that estimates concentration as being proportional to normalized signal intensity, e.g.:C(t)=k[S(t)−S(0)]/S(0)  [1]where S can be the MRI signal intensity, S(0) can be the signal intensity before contrast enhancement, and k can be a calibration constant (see, e.g., Wen J. G., et al., Evaluation of renal function in normal and hydronephrotic kidneys in rats using gadolinium diethylenetetramine-pentaacetic acid enhanced dynamic magnetic resonance imaging, The Journal of urology; 163(4):1264-1270 (2000); Jones R. A., et al., Dynamic contrast-enhanced MR urography in the evaluation of pediatric hydronephrosis: Part 1, functional assessment, AJR Am J Roentgenol; 185(6):1598-1607 (2005)). The use of this approach can be desired when combined with the use of low doses of contrast, due to an approximate linearity of the relationship between S(t) and C(t) for commonly used gradient echo acquisition sequences and C(t)<0.7 mM (see, e.g., Bokacheva L., et al., Quantitative determination of Gd-DTPA concentration in T(1)-weighted MR renography studies, Magn Reson Med; 57(6):1012-1018 (2007)). Alternatively, C(t) can be estimated from the longitudinal relaxation time T1(t). The estimation of T1 from signal intensity can usually be nonlinear and it can require the knowledge of S(0) and T1 (see, e.g., Bokacheva L., et al., Quantitative determination of Gd-DTPA concentration in T(1)-weighted MR renography studies, Magn Reson Med; 57(6):1012-1018 (2007); Bokacheva L., et al., Single breath-hold T1 measurement using low flip angle TrueFISP, Magn Reson Med; 55(5):1186-1190 (2006)). This approach (which can be called, e.g., a direct conversion) can be applicable to a wider range of C(t), and can yield accuracy better than approximately 10% in solid tissues (e.g., liver, kidneys, muscle) (see, e.g., Bokacheva L., et al., Quantitative determination of Gd-DTPA concentration in T(1)-weighted MR renography studies, Magn Reson Med; 57(6):1012-1018 (2007)). However, it can be significantly less accurate in the aorta and/or other major arteries, e.g., in regions used for measurement of AIF. A MR signal from these arteries can be subject to artifacts listed herein above, and signal errors can be further amplified when estimating tracer concentration by direct conversion, for example.
In an attempt to minimize the adverse effect of AIF distortions, Parker et al. (see, e.g., Parker G. J., et al., Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI, Magn Reson Med; 56(5):993-1000 (2006)) and Wang et al. (see, e.g., Wang Y., et al., Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data, Magn Reson Med; 59(5):1183-1189 (2008)) described that it is possible to average AIFs obtained from a group of controls and derived by direct conversion from measured signal intensities. For the analysis of patient data, rather than use actual patient AIFs, the population averaged AIF can be used instead. A rationale of averaging multiple AIFs can be to reduce random, uncorrelated sources of errors. However, systematic artifacts (such as inflow and partial volume effect) can likely distort the signals in a similar way across all individuals, preventing cancellation of these sources, for example. Moreover, the magnitude and the shape of AIF can depend on a patient's status (such as cardiac output and blood volume) and on the injection protocol used with the patient (see, e.g., Le Sech C., et al., Determination of pulmonary mean transit time and cardiac output using a one-dimensional model, Bulletin of mathematical biology; 58(6):1155-1170 (1996); Reiser U. J., Study of bolus geometry after intravenous contrast medium injection: dynamic and quantitative measurements (Chronogram) using an X-ray CT device, Journal of computer assisted tomography; 8(2):251-262 (1984); Hany T. F., et al., Optimization of contrast timing for breath-hold three-dimensional MR angiography, J Magn Reson Imaging; 7(3):551-556 (1997); Boos M., et al., Arterial first pass gadolinium-CM dynamics as a function of several intravenous saline flush and Gd volumes, J Magn Reson Imaging; 13(4):568-576 (2001)). For example, by disregarding differences between patients and/or protocols, the use of an averaged AIF can introduce additional sources of errors.
Thus, it may be beneficial to address and/or overcome at least some of the deficiencies described herein above.